

import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


__lowercase     : Dict       =						1E-4

if is_torch_available():
     import torch

     from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
     from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder



@require_torch
class 						_A     :





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=16   ,SCREAMING_SNAKE_CASE_=13   ,SCREAMING_SNAKE_CASE_=7   ,SCREAMING_SNAKE_CASE_=14   ,SCREAMING_SNAKE_CASE_=10   ,SCREAMING_SNAKE_CASE_=19   ,SCREAMING_SNAKE_CASE_=5   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=16   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_="gelu"   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=[1, 2, 3, 4, 5]   ,SCREAMING_SNAKE_CASE_=25   ,SCREAMING_SNAKE_CASE_=5   ,):

      '''simple docstring'''
      snake_case							:		Tuple        =		d_model
      snake_case							:		List[Any]        =		parent
      snake_case							:		Dict        =		batch_size
      snake_case							:		str        =		prediction_length
      snake_case							:		Optional[int]        =		context_length
      snake_case							:		Tuple        =		cardinality
      snake_case							:		Any        =		num_time_features
      snake_case							:		Optional[int]        =		lags_sequence
      snake_case							:		Dict        =		embedding_dimension
      snake_case							:		List[Any]        =		is_training
      snake_case							:		Union[str, Any]        =		hidden_size
      snake_case							:		Any        =		num_hidden_layers
      snake_case							:		Optional[Any]        =		num_attention_heads
      snake_case							:		Any        =		intermediate_size
      snake_case							:		int        =		hidden_act
      snake_case							:		Tuple        =		hidden_dropout_prob
      snake_case							:		str        =		attention_probs_dropout_prob

      snake_case							:		str        =		context_length
      snake_case							:		str        =		prediction_length + label_length
      snake_case							:		List[str]        =		label_length

      snake_case							:		Dict        =		moving_average
      snake_case							:		Union[str, Any]        =		autocorrelation_factor





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return AutoformerConfig(
          d_model=self.d_model   ,encoder_layers=self.num_hidden_layers   ,decoder_layers=self.num_hidden_layers   ,encoder_attention_heads=self.num_attention_heads   ,decoder_attention_heads=self.num_attention_heads   ,encoder_ffn_dim=self.intermediate_size   ,decoder_ffn_dim=self.intermediate_size   ,dropout=self.hidden_dropout_prob   ,attention_dropout=self.attention_probs_dropout_prob   ,prediction_length=self.prediction_length   ,context_length=self.context_length   ,label_length=self.label_length   ,lags_sequence=self.lags_sequence   ,num_time_features=self.num_time_features   ,num_static_categorical_features=1   ,cardinality=[self.cardinality]   ,embedding_dimension=[self.embedding_dimension]   ,moving_average=self.moving_average   ,)





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		config.context_length + max(config.lags_sequence    )

      snake_case							:		Tuple        =		ids_tensor([self.batch_size, 1]   ,config.cardinality[0]    )
      snake_case							:		Tuple        =		floats_tensor([self.batch_size, _past_length, config.num_time_features]    )
      snake_case							:		Union[str, Any]        =		floats_tensor([self.batch_size, _past_length]    )
      snake_case							:		str        =		floats_tensor([self.batch_size, _past_length]    ) > 0.5

      # decoder inputs
      snake_case							:		str        =		floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]    )
      snake_case							:		Tuple        =		floats_tensor([self.batch_size, config.prediction_length]    )

      snake_case							:		List[str]        =		{
          """past_values""": past_values,
          """static_categorical_features""": static_categorical_features,
          """past_time_features""": past_time_features,
          """past_observed_mask""": past_observed_mask,
          """future_time_features""": future_time_features,
          """future_values""": future_values,
      }
      return inputs_dict





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		self.get_config()
      snake_case							:		List[str]        =		self.prepare_autoformer_inputs_dict(SCREAMING_SNAKE_CASE_    )
      return config, inputs_dict





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		List[str]        =		self.prepare_config_and_inputs()
      return config, inputs_dict





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		AutoformerModel(config=SCREAMING_SNAKE_CASE_    ).to(SCREAMING_SNAKE_CASE_    ).eval()
      snake_case							:		Any        =		model(**SCREAMING_SNAKE_CASE_    )

      snake_case							:		Any        =		outputs.encoder_last_hidden_state
      snake_case							:		str        =		outputs.last_hidden_state

      with tempfile.TemporaryDirectory() as tmpdirname:
          snake_case							:		Union[str, Any]        =		model.get_encoder()
          encoder.save_pretrained(SCREAMING_SNAKE_CASE_    )
          snake_case							:		Any        =		AutoformerEncoder.from_pretrained(SCREAMING_SNAKE_CASE_    ).to(SCREAMING_SNAKE_CASE_    )

      snake_case      ,		snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		List[Any]        =		model.create_network_inputs(**SCREAMING_SNAKE_CASE_    )
      snake_case      ,		snake_case							:		str        =		model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]    )

      snake_case							:		Optional[int]        =		torch.cat(
          (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])   ,dim=-1   ,)
      snake_case							:		Optional[Any]        =		encoder(inputs_embeds=SCREAMING_SNAKE_CASE_    )[0]
      self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3    )

      snake_case							:		Optional[int]        =		(
          torch.mean(transformer_inputs[:, : config.context_length, ...]   ,dim=1    )
          .unsqueeze(1    )
          .repeat(1   ,config.prediction_length   ,1    )
      )
      snake_case							:		int        =		torch.zeros(
          [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]   ,device=enc_input.device   ,)

      snake_case							:		int        =		torch.cat(
          (
              torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)   ,dim=1    ),
              feature[:, config.context_length - config.label_length :, ...],
          )   ,dim=-1   ,)
      snake_case							:		Union[str, Any]        =		torch.cat(
          (
              torch.cat((trend_input[:, -config.label_length :, ...], mean)   ,dim=1    ),
              feature[:, config.context_length - config.label_length :, ...],
          )   ,dim=-1   ,)

      with tempfile.TemporaryDirectory() as tmpdirname:
          snake_case							:		Dict        =		model.get_decoder()
          decoder.save_pretrained(SCREAMING_SNAKE_CASE_    )
          snake_case							:		Optional[Any]        =		AutoformerDecoder.from_pretrained(SCREAMING_SNAKE_CASE_    ).to(SCREAMING_SNAKE_CASE_    )

      snake_case							:		Dict        =		decoder(
          trend=SCREAMING_SNAKE_CASE_   ,inputs_embeds=SCREAMING_SNAKE_CASE_   ,encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,)[0]

      self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3    )







@require_torch
class 						_A     (				snake_case						,    snake_case						,    unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      Dict					=   (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
  __lowerCamelCase			:      Tuple					=   (AutoformerForPrediction,) if is_torch_available() else ()
  __lowerCamelCase			:      Optional[Any]					=   {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
  __lowerCamelCase			:      str					=   False
  __lowerCamelCase			:      List[str]					=   False
  __lowerCamelCase			:      Dict					=   False
  __lowerCamelCase			:      List[str]					=   False
  __lowerCamelCase			:      str					=   False
  __lowerCamelCase			:      Optional[Any]					=   False





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		AutoformerModelTester(self    )
      snake_case							:		Optional[int]        =		ConfigTester(self   ,config_class=SCREAMING_SNAKE_CASE_   ,has_text_modality=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      self.config_tester.run_common_tests()





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		Dict        =		self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
          snake_case							:		int        =		model_class(SCREAMING_SNAKE_CASE_    )

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(SCREAMING_SNAKE_CASE_    )
              snake_case      ,		snake_case							:		Dict        =		model_class.from_pretrained(SCREAMING_SNAKE_CASE_   ,output_loading_info=SCREAMING_SNAKE_CASE_    )
          self.assertEqual(info["""missing_keys"""]   ,[]    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE_    )





  @unittest.skip(reason="""Model has no tokens embeddings"""    )
  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		inspect.signature(getattr(SCREAMING_SNAKE_CASE_   ,"""forward"""    )    )
      # The main input is the name of the argument after `self`
      snake_case							:		Tuple        =		list(model_signature.parameters.keys()    )[1]
      self.assertEqual(AutoformerModel.main_input_name   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		Any        =		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          snake_case							:		Tuple        =		model_class(SCREAMING_SNAKE_CASE_    )
          snake_case							:		Union[str, Any]        =		inspect.signature(model.forward    )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          snake_case							:		List[Any]        =		[*signature.parameters.keys()]

          snake_case							:		List[Any]        =		[
              """past_values""",
              """past_time_features""",
              """past_observed_mask""",
              """static_categorical_features""",
              """static_real_features""",
              """future_values""",
              """future_time_features""",
          ]

          if model.__class__.__name__ in ["AutoformerForPrediction"]:
              expected_arg_names.append("""future_observed_mask"""    )

          expected_arg_names.extend(
              [
                  """decoder_attention_mask""",
                  """head_mask""",
                  """decoder_head_mask""",
                  """cross_attn_head_mask""",
                  """encoder_outputs""",
                  """past_key_values""",
                  """output_hidden_states""",
                  """output_attentions""",
                  """use_cache""",
                  """return_dict""",
              ]    )

          self.assertListEqual(arg_names[: len(SCREAMING_SNAKE_CASE_    )]   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		Union[str, Any]        =		self.model_tester.prepare_config_and_inputs_for_common()
      snake_case							:		Optional[int]        =		True

      snake_case							:		Any        =		getattr(self.model_tester   ,"""seq_length"""   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		getattr(self.model_tester   ,"""decoder_seq_length"""   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		getattr(self.model_tester   ,"""encoder_seq_length"""   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		str        =		getattr(self.model_tester   ,"""d_model"""   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		getattr(self.model_tester   ,"""num_attention_heads"""   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		d_model // num_attention_heads

      for model_class in self.all_model_classes:
          snake_case							:		Dict        =		True
          snake_case							:		int        =		False
          snake_case							:		Tuple        =		True
          snake_case							:		Optional[int]        =		model_class(SCREAMING_SNAKE_CASE_    )
          model.to(SCREAMING_SNAKE_CASE_    )
          model.eval()
          with torch.no_grad():
              snake_case							:		List[Any]        =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )
          snake_case							:		Tuple        =		outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(SCREAMING_SNAKE_CASE_    )   ,self.model_tester.num_hidden_layers    )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          snake_case							:		List[Any]        =		True
          snake_case							:		Union[str, Any]        =		model_class(SCREAMING_SNAKE_CASE_    )
          model.to(SCREAMING_SNAKE_CASE_    )
          model.eval()
          with torch.no_grad():
              snake_case							:		List[str]        =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )
          snake_case							:		List[str]        =		outputs.encoder_attentions
          self.assertEqual(len(SCREAMING_SNAKE_CASE_    )   ,self.model_tester.num_hidden_layers    )

          self.assertListEqual(
              list(attentions[0].shape[-3:]    )   ,[self.model_tester.num_attention_heads, encoder_seq_length, dim]   ,)
          snake_case							:		Union[str, Any]        =		len(SCREAMING_SNAKE_CASE_    )

          snake_case							:		Tuple        =		7

          if "last_hidden_state" in outputs:
              correct_outlen += 1

          if "trend" in outputs:
              correct_outlen += 1

          if "past_key_values" in outputs:
              correct_outlen += 1  # past_key_values have been returned

          if "loss" in outputs:
              correct_outlen += 1

          if "params" in outputs:
              correct_outlen += 1

          self.assertEqual(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

          # decoder attentions
          snake_case							:		str        =		outputs.decoder_attentions
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,(list, tuple)    )
          self.assertEqual(len(SCREAMING_SNAKE_CASE_    )   ,self.model_tester.num_hidden_layers    )
          self.assertListEqual(
              list(decoder_attentions[0].shape[-3:]    )   ,[self.model_tester.num_attention_heads, decoder_seq_length, dim]   ,)

          # cross attentions
          snake_case							:		Tuple        =		outputs.cross_attentions
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,(list, tuple)    )
          self.assertEqual(len(SCREAMING_SNAKE_CASE_    )   ,self.model_tester.num_hidden_layers    )
          self.assertListEqual(
              list(cross_attentions[0].shape[-3:]    )   ,[self.model_tester.num_attention_heads, decoder_seq_length, dim]   ,)

      # Check attention is always last and order is fine
      snake_case							:		Union[str, Any]        =		True
      snake_case							:		Optional[int]        =		True
      snake_case							:		Union[str, Any]        =		model_class(SCREAMING_SNAKE_CASE_    )
      model.to(SCREAMING_SNAKE_CASE_    )
      model.eval()
      with torch.no_grad():
          snake_case							:		str        =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )

      self.assertEqual(out_len + 2   ,len(SCREAMING_SNAKE_CASE_    )    )

      snake_case							:		List[str]        =		outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

      self.assertEqual(len(SCREAMING_SNAKE_CASE_    )   ,self.model_tester.num_hidden_layers    )
      self.assertListEqual(
          list(self_attentions[0].shape[-3:]    )   ,[self.model_tester.num_attention_heads, encoder_seq_length, dim]   ,)





  @is_flaky()
  def    snake_case_       (  self    ):

      '''simple docstring'''
      super().test_retain_grad_hidden_states_attentions()







def       lowercase					(	__A :					List[str]="train-batch.pt"		)				->			Union[str, Any]:





    '''simple docstring'''




    snake_case							:		int        =		hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch""" ,    filename=a_ ,    repo_type="""dataset"""		)
    snake_case							:		Optional[Any]        =		torch.load(a_ ,    map_location=a_		)
    return batch



@require_torch
@slow
class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		str        =		AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[Any]        =		prepare_batch()

      with torch.no_grad():
          snake_case							:		List[str]        =		model(
              past_values=batch["""past_values"""]   ,past_time_features=batch["""past_time_features"""]   ,past_observed_mask=batch["""past_observed_mask"""]   ,static_categorical_features=batch["""static_categorical_features"""]   ,future_values=batch["""future_values"""]   ,future_time_features=batch["""future_time_features"""]   ,)[0]

      snake_case							:		Optional[Any]        =		torch.Size(
          (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)    )
      self.assertEqual(output.shape   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[int]        =		torch.tensor(
          [[0.35_93, -1.33_98, 0.63_30], [0.22_79, 1.53_96, -0.17_92], [0.04_50, 1.32_25, -0.23_35]]   ,device=SCREAMING_SNAKE_CASE_    )
      self.assertTrue(torch.allclose(output[0, :3, :3]   ,SCREAMING_SNAKE_CASE_   ,atol=SCREAMING_SNAKE_CASE_    )    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Dict        =		prepare_batch("""val-batch.pt"""    )
      with torch.no_grad():
          snake_case							:		Any        =		model(
              past_values=batch["""past_values"""]   ,past_time_features=batch["""past_time_features"""]   ,past_observed_mask=batch["""past_observed_mask"""]   ,static_categorical_features=batch["""static_categorical_features"""]   ,).encoder_last_hidden_state
      snake_case							:		int        =		torch.Size((64, model.config.context_length, model.config.d_model)    )
      self.assertEqual(output.shape   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		Union[str, Any]        =		torch.tensor(
          [[-0.07_34, -0.90_36, 0.83_58], [4.71_86, 2.41_13, 1.95_81], [1.79_53, 2.35_58, 1.29_70]]   ,device=SCREAMING_SNAKE_CASE_    )
      self.assertTrue(torch.allclose(output[0, :3, :3]   ,SCREAMING_SNAKE_CASE_   ,atol=SCREAMING_SNAKE_CASE_    )    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Dict        =		AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		prepare_batch("""val-batch.pt"""    )
      with torch.no_grad():
          snake_case							:		Optional[int]        =		model.generate(
              static_categorical_features=batch["""static_categorical_features"""]   ,past_time_features=batch["""past_time_features"""]   ,past_values=batch["""past_values"""]   ,future_time_features=batch["""future_time_features"""]   ,past_observed_mask=batch["""past_observed_mask"""]   ,)
      snake_case							:		Union[str, Any]        =		torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)    )
      self.assertEqual(outputs.sequences.shape   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		int        =		torch.tensor([3130.6763, 4056.5293, 7053.0786]   ,device=SCREAMING_SNAKE_CASE_    )
      snake_case							:		str        =		outputs.sequences.mean(dim=1    )
      self.assertTrue(torch.allclose(mean_prediction[0, -3:]   ,SCREAMING_SNAKE_CASE_   ,rtol=1E-1    )    )









'''simple docstring'''


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				unittest.TestCase			):
   _a    						=   PriorTransformer
   _a    						=   """hidden_states"""







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=0   )					->     Union[str, Any]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   @property
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     return (4, 8)







   @property
   def 		UpperCamelCase__      (						self   )					->     str:
     return (4, 8)







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       {
         'num_attention_heads': 2,
         'attention_head_dim': 4,
         'num_layers': 2,
         'embedding_dim': 8,
         'num_embeddings': 7,
         'additional_embeddings': 4,
     }
     __a								=       self.dummy_input
     return init_dict, inputs_dict







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a						,							__a								=       PriorTransformer.from_pretrained(
         'hf-internal-testing/prior-dummy'   ,				output_loading_info=UpperCamelCase   )
     self.assertIsNotNone(UpperCamelCase   )
     self.assertEqual(len(loading_info['missing_keys']   )   ,				0   )

     model.to(UpperCamelCase   )
     __a								=       model(**self.dummy_input   )[0]

     assert hidden_states is not None, "Make sure output is not None"







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a						,							__a								=       self.prepare_init_args_and_inputs_for_common()

     __a								=       self.model_class(**UpperCamelCase   )
     __a								=       inspect.signature(model.forward   )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __a								=       [*signature.parameters.keys()]

     __a								=       ['hidden_states', 'timestep']
     self.assertListEqual(arg_names[:2]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'   )
     __a								=       model.to(UpperCamelCase   )

     if hasattr(UpperCamelCase   ,				'set_default_attn_processor'   ):
       model.set_default_attn_processor()

     __a								=       self.get_dummy_seed_input()

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     __a								=       output[0, :5].flatten().cpu()
     print(UpperCamelCase   )

     # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
     # the expected output slices are not the same for CPU and GPU.
     __a								=       torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]   )
     self.assertTrue(torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				rtol=1e-2   )   )





@slow
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=1   ,				UpperCamelCase=768   ,				UpperCamelCase=77   ,				UpperCamelCase=0   )					->     List[str]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       batch_size
     __a								=       embedding_dim
     __a								=       num_embeddings

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]   )
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,				subfolder='prior'   )
     model.to(UpperCamelCase   )
     __a								=       self.get_dummy_seed_input(seed=UpperCamelCase   )

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     assert list(sample.shape   ) == [1, 768]

     __a								=       sample[0, :8].flatten().cpu()
     print(UpperCamelCase   )
     __a								=       torch.tensor(UpperCamelCase   )

     assert torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )



